Authors:
I. Ramírez
1
;
G. Galiano
2
;
N. Malpica
1
and
E. Schiavi
1
Affiliations:
1
King Rey Juan Carlos University, Spain
;
2
Oviedo University, Spain
Keyword(s):
Saliency Detection, Segmentation, Non-local Diffusion, Non-convex Optimization, Bilateral Filtering, P-laplacian Operator, MRI, FLAIR, Tumor, Edema.
Abstract:
Based on previous work on image classification and recent applications of non-local non-linear diffusion equations,
we propose a non-local p-laplacian variational model for saliency detection in digital images. Focusing
on the range 0 < p < 1 we also consider the regularized non-convex fluxes generated by the related hyper-laplacian
diffusion operators. With the aim of exploring the properties and potential applications of such
non-local, non-convex operators the model is applied to Magnetic Resonace Imaging (MRI) for Fluid Attenuated
Inversion Recovery image (FLAIR) modality showing promising numerical results. In this work Saliency
shall be understood as the relevant, outstanding region in a FLAIR image, which is commonly the brightest
part. It corresponds to a tumor and neighborhood edema. Our preliminary experiments show that the proposed
model can achieve very accurate results in this modality in terms of all the considered metrics.